A digital twin cold chain management method and system based on a multi-dimensional operation algorithm

Abstract

The application discloses a kind of digital twin cold chain management method and system based on multidimensional operation algorithm, belong to wisdom logistics and digital twin technical field, provide entity basis for the digital twin simulation of cold chain operation by the construction of cold chain digital twin model, it is convenient for the simulation and generation of cold chain management operation strategy, and by multidimensional operation algorithm, the cold chain operation target management model including multiple cold chain operation target management submodels is constructed, to respond to different operation scenes according to different cold chain operation target management submodel, the adaptation and compatibility to multiple operation scenes are realized, so that operation strategy is more accurate, more targeted;In addition, the operation scene judgment layer of cold chain operation target management model can realize independent scene recognition and submodel matching, greatly improve the adaptive ability of cold chain management, and through multiple simulation and target calculation, the strategy execution in digital level is realized, and the generation efficiency of cold chain management operation strategy is significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of smart logistics and digital twin technology, specifically relating to a digital twin cold chain management method and system based on a multi-dimensional operation algorithm. Background Technology

[0002] Cold chain logistics, as a crucial link in ensuring the quality and safety of temperature- and humidity-sensitive commodities such as food and pharmaceuticals, has become a key development direction in the logistics industry. Early cold chain management systems relied primarily on electrical refrigeration equipment and periodic manual inspections, resulting in inefficient management, slow response times, and weak risk control capabilities. However, with the development of sensor and information communication technologies, cold chain management systems have entered an information-based monitoring stage. By deploying temperature and humidity sensors, GPS, and other devices, remote and real-time data collection and visualization of environmental parameters and logistics trajectories have been achieved, significantly improving the status awareness and post-event traceability efficiency in cold chain management. In recent years, digital twin technology, as a cutting-edge paradigm integrating the Internet of Things, big data, artificial intelligence, and modeling simulation, has been introduced into the cold chain field. It aims to construct a virtual mirror that accurately maps and interacts in real-time with all elements, processes, and lifecycles of the physical cold chain system. Currently available digital twin cold chain management systems can initially achieve automated monitoring of temperature and humidity compliance, preliminary estimation of transportation timeliness, threshold warnings for abnormal events, and static planning of warehousing and distribution routes.

[0003] However, existing cold chain management systems based on digital twins still have significant limitations when facing complex and ever-changing actual operational needs. The core problem is that the decision-making and optimization algorithms built into the system tend to be singular and rigid. They usually adopt preset, general algorithm models (such as scheduling based on fixed rules or simple cost optimization models) and lack the ability to be flexibly configured according to specific business scenarios, development stages and strategic goals. This makes it difficult for existing cold chain management methods to dynamically adapt to different stages (different scenarios) or cope with differentiated operational needs when dealing with sudden market fluctuations.

[0004] As mentioned above, this paper discusses how to provide a digital twin cold chain management method and system based on multi-dimensional operation algorithms that can respond to different scenarios and improve the adaptability and strategy generation efficiency of cold chain management. Summary of the Invention

[0005] The purpose of this invention is to provide a digital twin cold chain management method and system based on a multi-dimensional operation algorithm to solve the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a digital twin cold chain management method based on a multi-dimensional operation algorithm, comprising: Obtain various basic model parameters of cold chain digital twins, construct original cold chain digital twin basic models using these parameters, and apply independent constraint loading and inter-model relationship loading to each original cold chain digital twin basic model to form the original cold chain digital twin model. Based on a multi-dimensional operation algorithm, a cold chain operation target management model is constructed. The cold chain operation target management model is then associated with each original cold chain digital twin model to form a cold chain operation target management model. The cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models. Obtain the current target operation scenario and current cold chain management data. Based on the current target operation scenario, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model. Then, use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. Multiple rounds of digital twin simulations are performed using the current cold chain digital twin models to obtain operational simulation data for each round. Based on the current scenario target management model, target calculations are performed on the operational simulation data obtained from each round of digital twin simulations to obtain the operational target results for each round of digital twin simulations. The operational target results for each round of digital twin simulations are compared, the optimal operational target result is selected, and the optimal cold chain management and operation strategy is generated and output based on the optimal operational target result.

[0007] In one possible design, multiple basic parameters of the cold chain digital twin are obtained. Using these parameters, original cold chain digital twin basic models are constructed. Independent constraints and inter-model relationships are loaded onto each original cold chain digital twin basic model to form the original cold chain digital twin model, including: Obtain a preset cold chain management process and cold chain management-related entities, and select key cold chain management entities from the cold chain management-related entities according to the cold chain management process; The actual status data of each of the key entities in cold chain management is collected through the Internet of Things, and the business entity data and business event data of each of the key entities in cold chain management are collected through the business system. The actual status data, the business entity data, and the business event data are classified according to their respective key cold chain management entities to form the original data objects corresponding to each key cold chain management entity. Each key entity in cold chain management is assigned a unique identifier to its original entity data object. Data unit formatting and time format standardization are performed on the original entity data object under each identifier. Outlier removal and missing value filling are performed on each original entity data object after data unit formatting and time format standardization to obtain the entity data object corresponding to each key entity in cold chain management. A corresponding cold chain digital twin model is defined for each key entity in cold chain management. The entity data objects corresponding to each key entity in cold chain management are used as the basic model parameters of the cold chain digital twin of each key entity in cold chain management. The basic model parameters of the cold chain digital twin of each key entity in cold chain management are filled into the cold chain digital twin model construction of each key entity in cold chain management to construct the original cold chain digital twin basic model. The basic parameters of the cold chain digital twin model include at least one of the following: cargo model parameters, inbound and outbound model parameters, truck model parameters, warehouse model parameters, customer model parameters, route model parameters, and freshness decay model parameters. Obtain the internal constraints of each key entity in cold chain management, and independently load constraints on each original cold chain digital twin basic model using the internal constraints of each key entity in cold chain management to obtain a pre-original cold chain digital twin model. The internal constraints of the entities are used to represent the internal correlation of the parameters of the cold chain digital twin basic model within the original cold chain digital twin basic model. According to the cold chain management process, the relationships between key entities in cold chain management are extracted. These relationships are then used to load the relationships between the key entities in cold chain management onto the pre-original cold chain digital twin models to form the original cold chain digital twin model. The relationships between the key entities in cold chain management are used to represent the external relationships between the pre-original cold chain digital twin models.

[0008] In one possible design, a cold chain operation target management model is constructed based on a multi-dimensional operation algorithm. This model is then associated with each original cold chain digital twin model to form a cold chain operation target management model, including: Acquire multiple preset cold chain operation scenarios, each of which includes scenario operation objectives, adjustable parameters, constraints, and core indicators. Based on the multi-dimensional operation algorithm, a corresponding target calculation layer is established using the scenario operation goals of each cold chain operation scenario; a variable configuration layer is established using the scenario operation adjustable parameters of each cold chain operation scenario; a target constraint layer is established using the scenario operation constraints of each cold chain operation scenario; and a result output layer is established using the scenario operation core indicators of each cold chain operation scenario. For each of the aforementioned cold chain operation scenarios, the target calculation layer, the variable configuration layer, the target constraint layer, and the result output layer are connected sequentially to construct a cold chain operation target management sub-model corresponding to each of the aforementioned cold chain operation scenarios; The various cold chain operation target management sub-models are integrated in parallel, and the target calculation layer of each cold chain operation target management sub-model is connected to the operation scenario judgment layer, and the result output layer of each cold chain operation target management sub-model is connected to the operation target result output layer to construct a cold chain operation target management model. The cold chain operation target management model obtains the input cold chain operation scenario through the operation scenario judgment layer and performs scenario judgment to select the corresponding cold chain operation target management sub-model for operation target result calculation.

[0009] In one possible design, the pre-set multiple cold chain operation scenarios include at least shopping festival operation scenarios, resource-constrained operation scenarios, and normal inventory operation scenarios; The operational objective of the shopping festival scenario is to maximize the number of orders received. The adjustable parameters of the shopping festival scenario include the daily warehouse replenishment volume and the daily additional cold chain trucks. The operational constraints of the shopping festival scenario include the limit on delayed delivery time, the constraint on the total order volume, and the limit on the maximum additional cost budget. The core operational indicators of the shopping festival scenario include the maximum daily resource turnover, compliance, timeliness, total cost, total loss, total goods circulation value, and total operating revenue. The operational objective of the resource-constrained operation scenario is to maximize resource utilization, while the operational objective of the normal inventory operation scenario is to maximize resource turnover and maximize operational revenue.

[0010] In one possible design, in the shopping festival operation scenario, the multi-dimensional operation algorithm is used to generate corresponding indicator calculation formulas for the core operation indicators of the shopping festival operation scenario. The maximum daily resource turnover index is calculated using the following formula (1): (1) In the formula, In the shopping festival operation scenario, the first sky, This indicates the maximum amount of goods received into the warehouse in a single day. This indicates the maximum daily sorting volume. This indicates the maximum daily outbound volume. This represents the maximum daily resource turnover indicator; The compliance indicators are calculated using the following formula (2): (2) In the formula, Indicates the first This shipment is the first one. This represents the total number of shipments during the shopping festival operation scenario. This indicates the duration for which the temperature and humidity of a single shipment of cold chain goods meet the standards. This indicates the total shipping time for a single shipment. This refers to the aforementioned compliance indicators; The timeliness index is calculated using the following formula (3): (3) In the formula, This indicates the number of shipment batches that met the timeliness target in the aforementioned shopping festival operation scenario. This indicates the total number of shipment batches in the aforementioned shopping festival operation scenario. Indicates the first Preset time for independent batch operation This indicates the actual total time consumed by the coordinated operation of multiple batches in the shopping festival operation scenario. and As a preset timeliness weight, This indicates the timeliness indicator; The total cost index is calculated using the following formula (4): (4) In the formula, This represents the basic inventory cost. This represents the average daily replenishment cost. Indicates unit warehousing cost, Indicates the effective storage volume per day. This indicates the average daily delivery cost per vehicle. This represents the total number of vehicles used in the shopping festival operation scenario. This indicates the number of days the shopping festival operation will last. This represents the total cost indicator; The total loss index is calculated using the following formula (5): (5) In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Category of goods This indicates the total variety of goods shipped during the shopping festival operation scenario. This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The value of goods of a certain type This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Loss rate of similar goods This represents the total loss index; The total value of goods turnover is calculated using the following formula (6): (6) In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Outbound volume of this type of goods This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The unit price of this type of goods. This represents the total value of goods turnover. The total operating revenue indicator is calculated using the following formula (7): - (7) In the formula, This represents the total operating revenue metric.

[0011] In one possible design, the current target operation scenario and current cold chain management data are obtained. Based on the current target operation scenario, a corresponding cold chain operation target management sub-model is selected from the cold chain operation target management model as the current scenario target management model. The parameters of each of the original cold chain digital twin models are updated using the current cold chain management data to obtain multiple current cold chain digital twin models, including: Obtain the current target operation scenario, input the current target operation scenario into the operation scenario judgment layer of the cold chain operation target management model, use the operation scenario judgment layer to judge the current target operation scenario, and obtain the scenario judgment result; Based on the scenario judgment result, the corresponding cold chain operation target management sub-model is selected from the cold chain operation target management model as the current scenario target management model; Obtain current cold chain management data and input it into the corresponding original cold chain digital twin model. Use the cold chain management data to update the basic model parameters of each original cold chain digital twin model to obtain the current model parameters of the cold chain digital twin. Then, update each original cold chain digital twin model to the current cold chain digital twin model using the current model parameters.

[0012] In one possible design, multiple rounds of digital twin simulations are performed using the various current cold chain digital twin models to obtain operational simulation data for each round. Based on the current scenario target management model, target calculations are performed on the operational simulation data obtained from each round of digital twin simulations to obtain the operational target results for each round. The operational target results from each round of digital twin simulations are compared, the optimal operational target result is selected, and based on the optimal operational target result, an optimal cold chain management and operation strategy is generated and output, including: Under the current target operation scenario, multiple rounds of digital twin simulation are performed on the current cold chain digital twin model, and single-round operation simulation data are extracted from the current cold chain digital twin model after each round of digital twin simulation. The operation simulation data obtained from each round of digital twin simulation are encoded and sorted to obtain the operation simulation dataset. The operation simulation dataset is input into the current scenario target management model. According to the encoding and sorting of the operation simulation data in each round of the operation simulation dataset, the target calculation is performed on each round of operation simulation data. The operation target results corresponding to the digital twin simulation of each round are output through the current scenario target management model. The operation target results include the core indicators of scenario operation and the scenario operation target under the current target operation scenario. Multi-objective optimization analysis is performed on the operational target results corresponding to each round of digital twin simulation to obtain optimization results. Based on the optimization results, the corresponding operational target results are selected from the operational target results corresponding to each round of digital twin simulation as the optimal operational target results. Based on the optimal operational target results, the corresponding digital twin simulation round is extracted as the optimal simulation. Based on the optimal simulation, the corresponding cold chain management operation strategy is generated as the optimal cold chain management operation strategy. The optimal cold chain management and operation strategy is sent to the cold chain control task distribution platform for strategy execution.

[0013] Secondly, this invention provides a digital twin cold chain management system based on a multi-dimensional operation algorithm, comprising: The digital twin creation unit is used to obtain various cold chain digital twin basic model parameters, construct the original cold chain digital twin basic model using the various cold chain digital twin basic model parameters, and perform independent constraint loading and inter-model association loading on each original cold chain digital twin basic model to form the original cold chain digital twin model. The cold chain management model building unit is used to construct a cold chain operation target management model based on a multi-dimensional operation algorithm. The cold chain operation target management model is associated with each original cold chain digital twin model to form a cold chain operation target management model. The cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models. The scenario model update unit is used to obtain the current target operation scenario and the current cold chain management data, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model based on the current target operation scenario as the current scenario target management model, and use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. The cold chain management and operation strategy generation unit is used to perform multiple rounds of digital twin simulation using each of the current cold chain digital twin models to obtain operational simulation data for each round of digital twin simulation. Based on the current scenario target management model, it performs target calculation on the operational simulation data obtained from each round of digital twin simulation to obtain the operational target results for each round of digital twin simulation. It compares the operational target results for each round of digital twin simulation, selects the optimal operational target result, and generates and outputs the optimal cold chain management and operation strategy based on the optimal operational target result.

[0014] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the digital twin cold chain management method based on a multidimensional operation algorithm as described in the first aspect or any possible design of the first aspect.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the digital twin cold chain management method based on a multidimensional operation algorithm as described in the first aspect or any possible design of the first aspect.

[0016] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform a digital twin cold chain management method based on a multidimensional operational algorithm as described in the first aspect or any possible design of the first aspect.

[0017] Beneficial Effects: This invention provides a digital twin cold chain management method and system based on a multi-dimensional operation algorithm, including: First, acquiring various basic model parameters of cold chain digital twins, constructing original cold chain digital twin basic models using these parameters, and independently loading constraints and inter-model associations onto each original cold chain digital twin basic model to form an original cold chain digital twin model; Second, constructing a cold chain operation target management model based on a multi-dimensional operation algorithm, and associating the cold chain operation target management model with each original cold chain digital twin model to form a cold chain operation target management model, wherein the cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models; Then, acquiring the current target operation scenario and current cold chain management data, based on... The current target operation scenario selects the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model, and updates the parameters of each of the original cold chain digital twin models using the current cold chain management data to obtain multiple current cold chain digital twin models. Finally, multiple rounds of digital twin simulation are performed using each of the current cold chain digital twin models to obtain the operation simulation data of each round of digital twin simulation. Based on the current scenario target management model, target calculation is performed on the operation simulation data obtained from each round of digital twin simulation to obtain the operation target results of each round of digital twin simulation. The operation target results of each round of digital twin simulation are compared to select the optimal operation target result, and based on the optimal operation target result, the optimal cold chain management operation strategy is generated and output. By constructing the original cold chain digital twin model, a physical foundation is provided for the digital twin simulation of cold chain operations, facilitating the simulation and generation of cold chain management and operation strategies. Furthermore, a cold chain operation target management model, comprising multiple cold chain operation target management sub-models, is constructed through a multi-dimensional operation algorithm. This model responds to different operation scenarios based on different cold chain operation target management sub-models, achieving adaptation and compatibility for multiple operation scenarios and making operation strategies more accurate and targeted. In addition, the operation scenario judgment layer of the cold chain operation target management model can achieve autonomous scenario recognition and sub-model matching, greatly improving the adaptability of cold chain management. Through multiple rounds of simulation and target calculation, digital-level strategy execution is realized, significantly improving the generation efficiency of cold chain management and operation strategies. Attached Figure Description

[0018] Figure 1 A flowchart illustrating the digital twin cold chain management method based on a multi-dimensional operation algorithm provided in this embodiment of the invention; Figure 2 A functional structure diagram of a digital twin cold chain management system based on a multi-dimensional operation algorithm provided in an embodiment of the present invention; Figure 3 A schematic diagram of the multi-layer architecture of a digital twin cold chain management system based on a multi-dimensional operation algorithm provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0020] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0021] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0022] Example: like Figure 1 As shown, the first aspect of this embodiment provides a digital twin cold chain management method based on a multi-dimensional operation algorithm, which may include, but is not limited to, the following steps: S1. Obtain various basic model parameters of cold chain digital twins, construct original basic models of cold chain digital twins using various basic model parameters of cold chain digital twins, and perform independent constraint loading and inter-model association loading on each original basic model of cold chain digital twins to form original cold chain digital twin models. In one possible implementation, step S1 involves obtaining various basic parameters of the cold chain digital twin model, constructing original cold chain digital twin models using these parameters, and applying independent constraint loading and inter-model relationship loading to each original cold chain digital twin model to form an original cold chain digital twin model. This can be decomposed into, but is not limited to, the following steps S11-S17, specifically including: S11. Obtain a preset cold chain management process and cold chain management related entities, so as to select key cold chain management entities from the cold chain management related entities according to the cold chain management process; S12. Collect the actual status data of each of the key entities in cold chain management through the Internet of Things, and collect the business entity data and business event data of each of the key entities in cold chain management through the business system; S13. Classify the actual status data, the business entity data and the business event data according to the corresponding key entities of cold chain management to form the original data objects corresponding to each key entity of cold chain management. S14. Add a unique identifier to the original entity data object of each key entity in cold chain management, perform data unit formatting and time format standardization on the original entity data object under each identifier, and remove outliers and fill missing values ​​on each original entity data object after completing data unit formatting and time format standardization to obtain the entity data object corresponding to each key entity in cold chain management. S15. Define a corresponding cold chain digital twin model construction for each key entity of cold chain management, use the entity data object corresponding to each key entity of cold chain management as the basic model parameter of the cold chain digital twin of each key entity of cold chain management, and fill the basic model parameter of the cold chain digital twin of each key entity of cold chain management into the cold chain digital twin model construction of each key entity of cold chain management accordingly to construct the original cold chain digital twin basic model. The basic parameters of the cold chain digital twin model include at least one of the following: cargo model parameters, inbound and outbound model parameters, truck model parameters, warehouse model parameters, customer model parameters, route model parameters, freshness decay model parameters, and intelligent unmanned sorting vehicle model parameters. S16. Obtain the internal constraints of each key entity in cold chain management, and independently load constraints on each original cold chain digital twin basic model using the internal constraints of each key entity in cold chain management to obtain a pre-original cold chain digital twin model. The internal constraints are used to represent the internal correlation of the parameters of the cold chain digital twin basic model within the original cold chain digital twin basic model. S17. According to the cold chain management process, extract the relationships between each key entity in cold chain management, and use the relationships between each key entity in cold chain management to load the relationships between each pre-original cold chain digital twin model to form an original cold chain digital twin model. The relationships between each key entity in cold chain management are used to represent the external relationships between each pre-original cold chain digital twin model.

[0023] S2. Based on the multi-dimensional operation algorithm, a cold chain operation target management model is constructed. The cold chain operation target management model is associated with each original cold chain digital twin model to form a cold chain operation target management model. The cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models. In one possible implementation, step S2 involves constructing a cold chain operation target management model based on a multi-dimensional operation algorithm. This model is then associated with each original cold chain digital twin model to form a cold chain operation target management model. This can be, but is not limited to, decomposed into the following steps S21-S24, specifically including: S21. Obtain multiple preset cold chain operation scenarios, wherein each cold chain operation scenario includes scenario operation objectives, adjustable parameters of scenario operation, scenario operation constraints and core indicators of scenario operation; S22. Based on the multi-dimensional operation algorithm, a corresponding target calculation layer is established using the scenario operation goals of each cold chain operation scenario, a variable configuration layer is established using the scenario operation adjustable parameters of each cold chain operation scenario, a target constraint layer is established using the scenario operation constraints of each cold chain operation scenario, and a result output layer is established using the scenario operation core indicators of each cold chain operation scenario. S23. For each of the cold chain operation scenarios, the target calculation layer, the variable configuration layer, the target constraint layer and the result output layer are connected in sequence to construct the cold chain operation target management sub-model corresponding to each of the cold chain operation scenarios; S24. Integrate the various cold chain operation target management sub-models in parallel, connect the target calculation layer of each cold chain operation target management sub-model to the operation scenario judgment layer, and connect the result output layer of each cold chain operation target management sub-model to the operation target result output layer to construct a cold chain operation target management model. The cold chain operation target management model obtains the input cold chain operation scenario through the operation scenario judgment layer and performs scenario judgment to select the corresponding cold chain operation target management sub-model for operation target result calculation.

[0024] In one possible implementation, in step S21, the preset multiple cold chain operation scenarios include at least a shopping festival operation scenario, a resource-constrained operation scenario, and a normal inventory operation scenario. The operational objective of the shopping festival scenario is to maximize the number of orders received. The adjustable parameters of the shopping festival scenario include the daily warehouse replenishment volume and the daily additional cold chain trucks. The operational constraints of the shopping festival scenario include the limit on delayed delivery time, the constraint on the total order volume, and the limit on the maximum additional cost budget. The core operational indicators of the shopping festival scenario include the maximum daily resource turnover, compliance, timeliness, total cost, total loss, total goods circulation value, and total operating revenue. The operational objective of the resource-constrained operation scenario is to maximize resource utilization, while the operational objective of the normal inventory operation scenario is to maximize resource turnover and maximize operational revenue.

[0025] In one possible implementation, in step S22, in the shopping festival operation scenario, the multi-dimensional operation algorithm is used to generate corresponding indicator calculation formulas for the core operation indicators of the shopping festival operation scenario. The maximum daily resource turnover index is calculated using the following formula (1): (1) In the formula, In the shopping festival operation scenario, the first sky, This indicates the maximum amount of goods received into the warehouse in a single day. This indicates the maximum daily sorting volume. This indicates the maximum daily outbound volume. This represents the maximum daily resource turnover indicator; The compliance indicators are calculated using the following formula (2): (2) In the formula, Indicates the first This shipment is the first one. This represents the total number of shipments during the shopping festival operation scenario. This indicates the duration for which the temperature and humidity of a single shipment of cold chain goods meet the standards. This indicates the total shipping time for a single shipment. This refers to the aforementioned compliance indicators; The timeliness index is calculated using the following formula (3): (3) In the formula, This indicates the number of shipment batches that met the timeliness target in the aforementioned shopping festival operation scenario. This indicates the total number of shipment batches in the aforementioned shopping festival operation scenario. Indicates the first Preset time for independent batch operation This indicates the actual total time consumed by the coordinated operation of multiple batches in the shopping festival operation scenario. and As a preset timeliness weight, This indicates the timeliness indicator; The total cost index is calculated using the following formula (4): (4) In the formula, This represents the basic inventory cost. This represents the average daily replenishment cost. Indicates unit warehousing cost, Indicates the effective storage volume per day. This indicates the average daily delivery cost per vehicle. This represents the total number of vehicles used in the shopping festival operation scenario. This indicates the number of days the shopping festival operation will last. This represents the total cost indicator; The total loss index is calculated using the following formula (5): (5) In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Category of goods This indicates the total variety of goods shipped during the shopping festival operation scenario. This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The value of goods of a certain type This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Loss rate of similar goods This represents the total loss index; The losses mentioned above are not limited to shipping losses, but also include losses in other cold chain links such as warehousing during the shopping festival operation scenario; The total value of goods turnover is calculated using the following formula (6): (6) In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Outbound volume of this type of goods This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The unit price of this type of goods. This represents the total value of goods turnover. The total operating revenue indicator is calculated using the following formula (7): - (7) In the formula, This represents the total operating revenue metric.

[0026] It should be noted that the digital twin cold chain management method provided in this embodiment sets up corresponding variable configuration layers in the cold chain operation target management sub-models corresponding to each of the aforementioned cold chain operation scenarios. The variable configuration layer allows operation managers to adjust windows intuitively based on key parameters (such as replenishment volume and vehicle allocation) through a graphical, templated, and parameterized algorithm configuration interface. The target calculation layer is used to realize the quantitative calculation of operation targets (such as maximizing order fulfillment rate), and the target constraint layer can configure different model hard constraints (such as maximum delay time and budget limit) according to different cold chain operation scenarios. This flexible and complete target management model enables the same set of digital twin infrastructure to be quickly matched and updated to adapt to different development and / or market stages of enterprises. Furthermore, it improves the adaptability and scalability of the system in response to changing market demands, and achieves agile model response capabilities.

[0027] Specifically, in different cold chain operation scenarios (shopping festival operation scenarios, resource-constrained operation scenarios, and normal inventory operation scenarios), this embodiment, when dealing with sudden high-order-value scenarios such as shopping festival operation scenarios, can conduct massive parallel simulations in a virtual environment based on pre-set supply guarantee targets and constraints. This allows for accurate prediction of fulfillment capacity boundaries, cost structures, and potential order limits under different replenishment strategies and capacity expansion plans. For example, the cold chain operation target management model provided in this embodiment can simulate the maximum order volume and corresponding benefit value that can be handled under different warehousing conditions. This provides precise data support for marketing strategy formulation and proactive deployment of supply chain resources, avoiding warehouse overload or stockouts, and significantly improving customer satisfaction and business revenue during shopping festivals. When dealing with resource-constrained operational scenarios, the cold chain operation target management model provided in this embodiment can configure a cold chain operation target management sub-model that maximizes resource utilization efficiency. In the simulation, it can prioritize orders, dynamically optimize the allocation of warehouse space, and coordinate vehicle route planning. This ensures that, with limited total resources, priority is given to high-value customers and urgent orders, while maximizing warehouse utilization and vehicle load factor. This allows for greater operational output with limited resources, achieving cost reduction and efficiency improvement. For normal inventory operation scenarios, the cold chain operation target management model provided in this embodiment can conduct simulations by setting a cold chain operation target management sub-model with resource turnover rate and overall operational efficiency as the primary objectives. This allows for analysis of key links affecting resource turnover (such as inbound and outbound efficiency and order batches) and quantitative evaluation of the impact of different variable adjustments (such as adjusting safety stock levels and optimizing sorting processes) on overall operational efficiency. This guides resources towards the links with the highest returns, driving continuous improvement in operational processes.

[0028] S3. Obtain the current target operation scenario and the current cold chain management data. Based on the current target operation scenario, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model. Then, use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. In one possible implementation, step S3 involves acquiring the current target operation scenario and current cold chain management data. Based on the current target operation scenario, a corresponding cold chain operation target management sub-model is selected from the cold chain operation target management model as the current scenario target management model. The parameters of each of the original cold chain digital twin models are then updated using the current cold chain management data to obtain multiple current cold chain digital twin models. This can be, but is not limited to, decomposed into the following steps S31-S33, specifically including: S31. Obtain the current target operation scenario, input the current target operation scenario into the operation scenario judgment layer of the cold chain operation target management model, use the operation scenario judgment layer to judge the current target operation scenario, and obtain the scenario judgment result; S32. Based on the scenario judgment result, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model; S33. Obtain the current cold chain management data and input the current cold chain management data into the corresponding original cold chain digital twin model, so as to use the cold chain management data to update the basic model parameters of each original cold chain digital twin model, obtain the current model parameters of the cold chain digital twin, and update each original cold chain digital twin model to the current cold chain digital twin model through the current model parameters of the cold chain digital twin.

[0029] In specific applications, the current cold chain management data described in this embodiment may include, but is not limited to, the time for goods to meet temperature and humidity standards, total time for goods, basic inventory cost, daily replenishment cost, unit warehousing cost, daily effective warehousing volume, daily average mileage cost, unit goods loading and unloading fee, daily actual carrying volume, and goods loss rate, as well as other input parameters required by the target management model for the current scenario.

[0030] S4. Perform multiple rounds of digital twin simulation using each of the current cold chain digital twin models to obtain operational simulation data for each round of digital twin simulation. Based on the current scenario target management model, perform target calculations on the operational simulation data obtained from each round of digital twin simulation to obtain the operational target results for each round of digital twin simulation. Compare the operational target results for each round of digital twin simulation, select the optimal operational target result, and generate and output the optimal cold chain management operation strategy based on the optimal operational target result.

[0031] In one possible implementation, in step S4, multiple rounds of digital twin simulation are performed using each of the current cold chain digital twin models to obtain operational simulation data for each round of digital twin simulation. Based on the current scenario target management model, target calculations are performed on the operational simulation data obtained from each round of digital twin simulation to obtain the operational target results for each round of digital twin simulation. The operational target results for each round of digital twin simulation are compared to select the optimal operational target result. Based on the optimal operational target result, the optimal cold chain management and operation strategy is generated and output. This can be decomposed into, but is not limited to, the following steps S41-S44, specifically including: S41. Under the current target operation scenario, perform multiple rounds of digital twin simulation on the current cold chain digital twin model, and extract single-round operation simulation data from the current cold chain digital twin model after each round of digital twin simulation. Encode and sort the operation simulation data obtained from each round of digital twin simulation to obtain the operation simulation dataset. S42. Input the operation simulation dataset into the current scenario target management model, and calculate the target for each round of operation simulation data according to the encoding and sorting of the operation simulation data in the operation simulation dataset. Then, output the operation target results corresponding to the digital twin simulation of each round through the current scenario target management model. The operation target results include the core indicators of scenario operation and the scenario operation target under the current target operation scenario. S43. Perform multi-objective optimization analysis on the operational target results corresponding to each round of digital twin simulation to obtain optimization results. Select the corresponding operational target results from the operational target results corresponding to each round of digital twin simulation as the optimal operational target results based on the optimization results. Extract the corresponding digital twin simulation round as the optimal simulation based on the optimal simulation. Generate the corresponding cold chain management operation strategy as the optimal cold chain management operation strategy based on the optimal simulation. S44. Send the optimal cold chain management and operation strategy to the cold chain control task distribution platform for strategy execution.

[0032] It should be noted that the digital twin cold chain management method provided in this embodiment transforms business objectives into a computable model and conducts extensive simulations in a simulated digital environment constructed at the digital twin level. This enables a comprehensive evaluation of the potential results and risks of various strategy combinations. It not only outputs an optimal numerical solution for the objective but also provides detailed strategy indicators (core operational indicators for the scenario). This allows the digital twin cold chain management method in this embodiment to provide a decision-making environment with realistic scenario prediction capabilities for cold chain logistics management. It transforms strategy selection and decision-making from being based on fuzzy experience to being based on clear data simulation and comparison, greatly reducing decision-making risks and trial-and-error costs, and improving the efficiency, reliability, and accuracy of the entire cold chain management.

[0033] Furthermore, in this embodiment, multi-objective optimization analysis is performed on the operational target results corresponding to each round of digital twin simulation. This is actually a multi-objective comparison of the generated core operational indicators and operational targets of the scenario, in order to optimize the overall multi-objectives and ensure the comprehensive benefits and feasibility of the final optimal cold chain management and operation strategy. Therefore, when comparing the operational target results, each indicator can be compared step by step according to the preset result indicator priority to ensure the high-efficiency output of the main indicators. Alternatively, corresponding weights can be set for each indicator to obtain the comprehensive benefit score of each round of simulation strategy. This allows for efficient and holistic analysis and evaluation of each strategy scheme, resulting in a more holistic and globally beneficial operational strategy as the optimal cold chain management and operation strategy.

[0034] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the digital twin cold chain management method based on a multi-dimensional operation algorithm described in the first aspect of the embodiment, including: The digital twin creation unit is used to obtain various cold chain digital twin basic model parameters, construct the original cold chain digital twin basic model using the various cold chain digital twin basic model parameters, and perform independent constraint loading and inter-model association loading on each original cold chain digital twin basic model to form the original cold chain digital twin model. The cold chain management model building unit is used to construct a cold chain operation target management model based on a multi-dimensional operation algorithm. The cold chain operation target management model is associated with each original cold chain digital twin model to form a cold chain operation target management model. The cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models. The scenario model update unit is used to obtain the current target operation scenario and the current cold chain management data, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model based on the current target operation scenario as the current scenario target management model, and use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. The cold chain management and operation strategy generation unit is used to perform multiple rounds of digital twin simulation using each of the current cold chain digital twin models to obtain operational simulation data for each round of digital twin simulation. Based on the current scenario target management model, it performs target calculation on the operational simulation data obtained from each round of digital twin simulation to obtain the operational target results for each round of digital twin simulation. It compares the operational target results for each round of digital twin simulation, selects the optimal operational target result, and generates and outputs the optimal cold chain management and operation strategy based on the optimal operational target result.

[0035] like Figure 3 As shown, it should be noted that the digital twin cold chain management system provided in this embodiment can be configured into a multi-layer architecture in practical applications, specifically including: a front-end layer, a middle platform layer, a basic service layer, and an infrastructure layer; wherein, the front-end layer is used to constitute the front-end application, realizing functions such as mobile communication and visualization display; the middle platform layer is used to realize data monitoring and control terminal association management functions such as order management, warehouse management, and transportation management; the basic service layer is used to realize key services and execution functions such as model updates, simulation calculations, and result comparisons; and the infrastructure layer acts as a virtual server, maintaining the calculation and implementation of the overall system architecture.

[0036] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0037] like Figure 4 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the digital twin cold chain management method based on a multi-dimensional operation algorithm as described in the first aspect of the embodiment.

[0038] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0039] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee transceiver (a low-power LAN protocol based on the IEEE 802.15.4 standard), a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0040] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0041] The fourth aspect of this embodiment provides a storage medium that stores instructions for a digital twin cold chain management method based on a multi-dimensional operation algorithm as described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when executed on a computer, perform the digital twin cold chain management method based on a multi-dimensional operation algorithm as described in the first aspect of the embodiment.

[0042] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0043] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0044] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the digital twin cold chain management method based on a multi-dimensional operation algorithm as described in the first aspect of this embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0045] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A digital twin cold chain management method based on a multi-dimensional operation algorithm, characterized in that, include: Multiple cold chain digital twin basic model parameters are obtained. Using these parameters, original cold chain digital twin basic models are constructed. Independent constraints and inter-model relationships are loaded onto each original cold chain digital twin basic model to form the original cold chain digital twin model. The cold chain digital twin basic model parameters include at least one of the following: cargo model parameters, inbound / outbound model parameters, truck model parameters, warehouse model parameters, customer model parameters, route model parameters, and freshness decay model parameters. Acquire multiple preset cold chain operation scenarios, each of which includes scenario operation objectives, adjustable parameters, constraints, and core indicators. Based on the multi-dimensional operation algorithm, a corresponding target calculation layer is established using the scenario operation goals of each cold chain operation scenario; a variable configuration layer is established using the scenario operation adjustable parameters of each cold chain operation scenario; a target constraint layer is established using the scenario operation constraints of each cold chain operation scenario; and a result output layer is established using the scenario operation core indicators of each cold chain operation scenario. For each of the aforementioned cold chain operation scenarios, the target calculation layer, the variable configuration layer, the target constraint layer, and the result output layer are connected sequentially to construct a cold chain operation target management sub-model corresponding to each of the aforementioned cold chain operation scenarios; The various cold chain operation target management sub-models are integrated in parallel, and the target calculation layer of each cold chain operation target management sub-model is connected to the operation scenario judgment layer, and the result output layer of each cold chain operation target management sub-model is connected to the operation target result output layer to construct a cold chain operation target management model. Obtain the current target operation scenario and current cold chain management data. Based on the current target operation scenario, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model. Then, use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. Multiple rounds of digital twin simulations are performed using the current cold chain digital twin models to obtain operational simulation data for each round. Based on the current scenario target management model, target calculations are performed on the operational simulation data obtained from each round of digital twin simulations to obtain the operational target results for each round of digital twin simulations. The operational target results for each round of digital twin simulations are compared, the optimal operational target result is selected, and the optimal cold chain management and operation strategy is generated and output based on the optimal operational target result.

2. The digital twin cold chain management method based on a multi-dimensional operation algorithm according to claim 1, characterized in that, Obtain various basic parameters for cold chain digital twin models, and construct corresponding original cold chain digital twin basic models using these parameters. Then, independently load constraints and inter-model relationships onto each original cold chain digital twin basic model to form the original cold chain digital twin model, including: Obtain a preset cold chain management process and cold chain management-related entities, and select key cold chain management entities from the cold chain management-related entities according to the cold chain management process; The actual status data of each of the key entities in cold chain management is collected through the Internet of Things, and the business entity data and business event data of each of the key entities in cold chain management are collected through the business system. The actual status data, the business entity data, and the business event data are classified according to their respective key cold chain management entities to form the original entity data objects corresponding to each key cold chain management entity. Each key entity in cold chain management is assigned a unique identifier to its original entity data object. Data unit formatting and time format standardization are performed on the original entity data object under each identifier. Outlier removal and missing value filling are performed on each original entity data object after data unit formatting and time format standardization to obtain the entity data object corresponding to each key entity in cold chain management. Define the corresponding cold chain digital twin model construction for each key entity of cold chain management, use the entity data object corresponding to each key entity of cold chain management as the basic model parameter of the cold chain digital twin of each key entity of cold chain management, and fill the basic model parameter of the cold chain digital twin of each key entity of cold chain management into the construction of the cold chain digital twin model of each key entity of cold chain management to build the original cold chain digital twin basic model. Obtain the internal constraints of each key entity in cold chain management, and independently load constraints on each original cold chain digital twin basic model using the internal constraints of each key entity in cold chain management to obtain a pre-original cold chain digital twin model. The internal constraints of the entities are used to represent the internal correlation of the parameters of the cold chain digital twin basic model within the original cold chain digital twin basic model. According to the cold chain management process, the relationships between key entities in cold chain management are extracted. These relationships are then used to load the relationships between the key entities in cold chain management onto the pre-original cold chain digital twin models to form the original cold chain digital twin model. The relationships between the key entities in cold chain management are used to represent the external relationships between the pre-original cold chain digital twin models.

3. The digital twin cold chain management method based on a multi-dimensional operation algorithm according to claim 1, characterized in that, The pre-set multiple cold chain operation scenarios include at least shopping festival operation scenarios, resource-constrained operation scenarios, and normal inventory operation scenarios; The operational objective of the shopping festival scenario is to maximize the number of orders received. The adjustable parameters of the shopping festival scenario include the daily warehouse replenishment volume and the daily additional cold chain trucks. The operational constraints of the shopping festival scenario include the limit on delayed delivery time, the constraint on the total order volume, and the limit on the maximum additional cost budget. The core operational indicators of the shopping festival scenario include the maximum daily resource turnover, compliance, timeliness, total cost, total loss, total goods circulation value, and total operating revenue. The operational objective of the resource-constrained operation scenario is to maximize resource utilization, while the operational objective of the normal inventory operation scenario is to maximize resource turnover and maximize operational revenue.

4. The digital twin cold chain management method based on multi-dimensional operation algorithm according to claim 3, characterized in that, In the shopping festival operation scenario, the multi-dimensional operation algorithm is used to generate corresponding indicator calculation formulas for the core operation indicators of the shopping festival operation scenario; The maximum daily resource turnover index is calculated using the following formula (1): （1） In the formula, This indicates the maximum amount of goods received into the warehouse in a single day. This indicates the maximum daily sorting volume. This indicates the maximum daily outbound volume. This represents the maximum daily resource turnover indicator. It is a minimum value function; The compliance indicators are calculated using the following formula (2): （2） In the formula, Indicates the first This shipment is the first one. This represents the total number of shipments during the shopping festival operation scenario. This indicates the duration for which the temperature and humidity of a single shipment of cold chain goods meet the standards. This indicates the total shipping time for a single shipment. This refers to the aforementioned compliance indicators; The timeliness index is calculated using the following formula (3): （3） In the formula, This indicates the number of shipment batches that met the timeliness target in the aforementioned shopping festival operation scenario. This indicates the total number of shipment batches in the aforementioned shopping festival operation scenario. Indicates the first Preset time for independent batch operation This indicates the actual total time consumed by the coordinated operation of multiple batches in the shopping festival operation scenario. and As a preset timeliness weight, This indicates the timeliness indicator, and and The sum of is 1; The total cost index is calculated using the following formula (4): （4） In the formula, This represents the basic inventory cost. This represents the average daily replenishment cost. Indicates unit warehousing cost, Indicates the effective storage volume per day. This indicates the average daily delivery cost per vehicle. This represents the total number of vehicles used in the shopping festival operation scenario. This indicates the number of days the shopping festival operation will last. This represents the total cost indicator; The total loss index is calculated using the following formula (5): （5） In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Category of goods This indicates the total variety of goods shipped during the shopping festival operation scenario. This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The value of goods of a certain type This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Loss rate of similar goods This represents the total loss index; The total value of goods turnover is calculated using the following formula (6): （6） In the formula, This indicates the number of shipments made in the aforementioned shopping festival operation scenario. Outbound volume of this type of goods This indicates the number of shipments made in the aforementioned shopping festival operation scenario. The unit price of this type of goods. This represents the total value of goods turnover. The total operating revenue indicator is calculated using the following formula (7): （7） In the formula, This represents the total operating revenue metric.

5. The digital twin cold chain management method based on a multi-dimensional operation algorithm according to claim 1, characterized in that, Obtain the current target operation scenario and current cold chain management data. Based on the current target operation scenario, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model as the current scenario target management model. Then, use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models, including: Obtain the current target operation scenario, input the current target operation scenario into the operation scenario judgment layer of the cold chain operation target management model, use the operation scenario judgment layer to judge the current target operation scenario, and obtain the scenario judgment result; Based on the scenario judgment result, the corresponding cold chain operation target management sub-model is selected from the cold chain operation target management model as the current scenario target management model; Obtain current cold chain management data and input it into the corresponding original cold chain digital twin model. Use the cold chain management data to update the basic model parameters of each original cold chain digital twin model to obtain the current model parameters of the cold chain digital twin. Then, update each original cold chain digital twin model to the current cold chain digital twin model using the current model parameters.

6. The digital twin cold chain management method based on a multi-dimensional operation algorithm according to claim 1, characterized in that, Multiple rounds of digital twin simulations are performed using the current cold chain digital twin models to obtain operational simulation data for each round. Based on the current scenario target management model, target calculations are performed on the operational simulation data obtained from each round of digital twin simulations to obtain the operational target results for each round. The operational target results from each round of digital twin simulations are compared, the optimal operational target result is selected, and based on the optimal operational target result, an optimal cold chain management and operation strategy is generated and output, including: Under the current target operation scenario, multiple rounds of digital twin simulation are performed on the current cold chain digital twin model, and single-round operation simulation data are extracted from the current cold chain digital twin model after each round of digital twin simulation. The operation simulation data obtained from each round of digital twin simulation are encoded and sorted to obtain the operation simulation dataset. The operation simulation dataset is input into the current scenario target management model. According to the encoding and sorting of the operation simulation data in each round of the operation simulation dataset, the target calculation is performed on each round of operation simulation data. The operation target results corresponding to the digital twin simulation of each round are output through the current scenario target management model. The operation target results include the core indicators of scenario operation and the scenario operation target under the current target operation scenario. Multi-objective optimization analysis is performed on the operational target results corresponding to each round of digital twin simulation to obtain optimization results. Based on the optimization results, the corresponding operational target results are selected from the operational target results corresponding to each round of digital twin simulation as the optimal operational target results. Based on the optimal operational target results, the corresponding digital twin simulation round is extracted as the optimal simulation. Based on the optimal simulation, the corresponding cold chain management operation strategy is generated as the optimal cold chain management operation strategy. The optimal cold chain management and operation strategy is sent to the cold chain control task distribution platform for strategy execution.

7. A digital twin cold chain management system based on a multi-dimensional operational algorithm, characterized in that, The digital twin cold chain management method based on a multi-dimensional operation algorithm as described in any one of claims 1 to 6 includes: The digital twin creation unit is used to obtain various cold chain digital twin basic model parameters, construct the original cold chain digital twin basic model using the various cold chain digital twin basic model parameters, and perform independent constraint loading and inter-model association loading on each original cold chain digital twin basic model to form the original cold chain digital twin model. The cold chain management model building unit is used to construct a cold chain operation target management model based on a multi-dimensional operation algorithm. The cold chain operation target management model is associated with each original cold chain digital twin model to form a cold chain operation target management model. The cold chain operation target management model includes an operation scenario judgment layer and multiple cold chain operation target management sub-models. The scenario model update unit is used to obtain the current target operation scenario and the current cold chain management data, select the corresponding cold chain operation target management sub-model from the cold chain operation target management model based on the current target operation scenario as the current scenario target management model, and use the current cold chain management data to update the parameters of each of the original cold chain digital twin models to obtain multiple current cold chain digital twin models. The cold chain management and operation strategy generation unit is used to perform multiple rounds of digital twin simulation using each of the current cold chain digital twin models to obtain operational simulation data for each round of digital twin simulation. Based on the current scenario target management model, it performs target calculation on the operational simulation data obtained from each round of digital twin simulation to obtain the operational target results for each round of digital twin simulation. It compares the operational target results for each round of digital twin simulation, selects the optimal operational target result, and generates and outputs the optimal cold chain management and operation strategy based on the optimal operational target result.

8. An electronic device, characterized in that, The system includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the digital twin cold chain management method based on a multi-dimensional operation algorithm as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the digital twin cold chain management method based on a multi-dimensional operation algorithm as described in any one of claims 1 to 6.

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